Abstract
With the development of computer technology, information technology, and 3D reconstruction technology of the medical human body, 3D virtual digital human body technology for human health has been widely used in various fields of medicine, especially in teaching students of application and anatomy. Its advantage is that it can view 3D human anatomy models from any angle and can be cut in any direction. In this paper, we propose an improved algorithm based on a hybrid density network and an element-level attention mechanism. The hybrid density network is used to generate feasible hypotheses for multiple 3D poses, solve the ambiguity problem in pose reasoning from 2D to 3D, and improve the performance of the network by adding the AReLU function combined with an element-wise attention mechanism. Teaching students in anatomy makes students' learning more convenient and teachers' teaching explanations more vivid. Comparative experiments show that the accuracy of 3D human pose estimation using a single image input is better than the other two-stage methods.
Highlights
Human specimens have long played an important role as a nonrenewable and precious resource for medical theory in the process of teaching and scientific research [1]
Due to many factors such as preservative preservation conditions and cadaver sources, there is an abnormal lack of cadaveric specimens for teaching and scientific research, and the preservatives are toxic and harmful, which seriously affect people’s physical and mental health [2]
We propose an improved algorithm based on a hybrid density network and EAM. e hybrid density network is used to generate feasible hypotheses for multiple 3D poses, to solve the ambiguity problem when reasoning from 2D to 3D poses, and to improve the performance of the network by adding an AReLU function combining the element-wise attention mechanism and the ReLU activation function, in order to provide information and relevant morphological data for the development of human specimen digitization [10]
Summary
Human specimens have long played an important role as a nonrenewable and precious resource for medical theory in the process of teaching and scientific research [1]. During the development of human specimen digitization, many scholars have used MRI, CT, and other medical imaging equipment to obtain data information of human tissue structures for the study of human 3D structure reconstruction and have achieved certain results [4]. E attention mechanism, as the name suggests, is a technology that enables the model to focus on important information and fully learn and absorb it. E hybrid density network is used to generate feasible hypotheses for multiple 3D poses, to solve the ambiguity problem when reasoning from 2D to 3D poses, and to improve the performance of the network by adding an AReLU function combining the element-wise attention mechanism and the ReLU activation function, in order to provide information and relevant morphological data for the development of human specimen digitization [10] We propose an improved algorithm based on a hybrid density network and EAM. e hybrid density network is used to generate feasible hypotheses for multiple 3D poses, to solve the ambiguity problem when reasoning from 2D to 3D poses, and to improve the performance of the network by adding an AReLU function combining the element-wise attention mechanism and the ReLU activation function, in order to provide information and relevant morphological data for the development of human specimen digitization [10]
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